1) Field of the Invention
The present invention relates to an apparatus and a method for optimizing substance libraries comprising at least two substances.
More precisely, the invention relates to a computer-aided method which permits the successive material optimization of non-molecular substance libraries with respect to a defined application and/or the optimization of the test parameters which are relevant to the application or are important to the process of selected substances within a predefined parameter space or a parameter space resulting during the optimization (altered with respect to the predefined parameter space), an apparatus suitable therefor, comprising a substrate, the substance library per se which can be produced in this way and a computer program for controlling the inventive method.
In the context of the inventive method it is possible to optimize substance libraries comprising molecular and non-molecular substances.
2) Description of Related Art
The preparation of substance libraries in pharmaceutical combinatorial research generally begins with designing a hypothesis regarding the interaction of an active compound molecule (for example a ligand) with a biological target (receptor). The type and strength of interaction here are associated with discrete structural properties of the active compound molecule (SAR—structure activity relationship). The term “structural property” includes here, for example, topology, conformation, spatial arrangement of substituents or electronic configuration of the active compound molecules. These “structural properties” are incorporated as descriptors, that is to say as parameters for describing the interaction(s) between active compound molecule and receptor. The classical combinatorial approach is based on the systematic change (permutation) of the structural properties of one or more (molecular) organic base skeletons. This presupposes the existence of suitable organic building blocks (for example a methyl or phenyl group) which react under defined synthesis conditions with the base skeleton to form a target molecule of the planned substance library. After determining the activity of the molecules synthesized within the substance library against a biological target, the hypothesis originally made is revised with the objective of producing an optimized substance library.
U.S. Pat. Nos. 5,901,069 and 5,463,564 disclose systems and methods for the at least partial automated generation of (chemical) compounds having desired chemical or bioactive properties.
There, starting from an initial hypothesis relating to the interactions of interest and the structural features required for this, a computer-aided process is carried out which during each iteration comprises the following steps:
(1) a library consisting of a plurality of compounds is robotically generated in accordance with robotic system instructions;
(2) the compounds in the library are analysed in order to identify those compounds which have the desired useful properties;
(3) structure-activity data are utilized in order to select compounds which are to be synthesized in the next iteration; and
(4) new robotic system instructions are generated by the experimenter which control the synthesis of the compounds in the library for the next iteration.
As an aid to refining the hypothesis initially made in pharmaceutical research, suitable software is available for modeling and visualizing molecules, or else mathematical/statistical software which comprises, for example, regression methods, for example linear single or multiple regression, for quantifying the structure-activity relationship (QSAR—quantitative structure activity relationships). In the literature, corresponding computer-aided methods for pharmaceutical applications are also termed CADD (computer-aided drug design) or CAMD (computer-aided molecular design).
The systems and methods disclosed in these publications are not fully automated for optimization, that is to say the experimenter must intervene at one or more time points when the method is being carried out. In addition, the change(s) made to the “structural property” in these methods is (are) always carried out by varying discrete states, that is to say for example dependent on the variation of a substitution pattern, for example methyl→ethyl→propyl→ . . .
In summary, the above described implies that the targeted optimization, which is not based exclusively on trial and error, of molecular organic libraries is based on the following principles:
a) assuming a relationship between structure and activity of a molecule in question (SAR);
b) existence of suitable synthetic building blocks;
c) variation of discrete molecular properties;
d) use of molecular descriptors.
Whereas points a) and d) do not represent a fundamental precondition for the combinatorial variation of an organic base skeleton, points b) and c) are an essential necessity therefor.
Since in the case of non-molecular substances the required relationships between structure and activity of the substances are often unknown, and corresponding molecular descriptors do not exist, for the complete integration of the generation of non-molecular substance libraries, recourse cannot be made to the methods for library optimization in pharmaceutical active compound research.
The preparation and testing of non-molecular substance libraries outside pharmaceutical research is described in a range of publications. In this sector, production, testing and evaluation of the substance libraries are represented as separate process steps. However, the complete integration of the individual process steps in to a joint software environment has not yet been described to date, as can be seen from the following summary of the relevant prior art:
Danielson et al. describe a combinatorial method for discovering and optimizing luminescent substances (Nature, vol. 389, p. 944, 1997). The method of Danielson comprises the automated production of a first substance library, testing the first substance library in order to identify lead materials, and designing and synthesizing newly optimized substance libraries by the experimenter on the basis of the composition of the lead substances identified. According to Danielson, the combinatorial exploration requires different iterations in order to optimize the composition and production for a defined application. The iterative optimization according to Danielson is based, however, solely on the intuition of the experimenter without the use of computers or the use of software-controlled optimization methods.
WO 00/23921 relates to a computer-controlled method for generating a library design for a combinatorial material library which comprises:
defining one or more sources and one or more destinations, each source being an electronic data point representing a component for preparing the combinatorial library and each destination being an electronic data point representing an arrangement of cells;
receiving an input which defines the first mapping, this first mapping being electronic data defining a distribution pattern for assigning a component to cells in the arrangement, the distribution pattern defining a minimum and a maximum amount of the component and a gradient between the minimum and the maximum amounts of the component across the multiplicity of the cells;
using the first mapping to calculate a composition of one or more materials to be assigned to one or more of the cells; and
generating a data file for defining the library design, the data file comprising electronic data representing the sources, the destinations and the mapping.
According to this publication, therefore, only the automated production of material libraries is described or claimed.